An17 exhibitor insights_tuesday_whats hiding in your point of sale data

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17 What’s Hiding in Your Point of

Sale Data?

IRAD BEN-GAL, Professor and Chairman, Stanford University/C-B4JOE GAUTHIER, Director, Operations, Wesco, Inc.

MIKI CISIC, Director, Sales, C-B4 Analytics

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The content of this document is C-B4 Confidential & Proprietary. If you are not the intended recipient and have received this document, any use or distribution is prohibited. Please notify [email protected] immediately by e-mail and delete this message from your computer system.

What's Hiding in Your Point of Sale Data?Explore the differences between market basket analysis vsin-store consumer behavior

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Basket Analysis (a.k.a Affinity Analysis)• Which group of items are likely (or less

likely) to be purchased together? For example, beer & potato-chips or shampoo & conditioner….

• Provides a better understanding of the individual purchase behavior of the customer (“impulsive customer purchase”).

• Well established & helpful in many applications

P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S3 |

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Market Basket Analysis vs. In-store Purchase Pattern Analysis


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BreadButter

Milk DiapersBeer{Bread & Butter Milk}

Mon #1

Mon #2

Tue #1

Tue #2

Wed #1

Wed #2

Thu #1

Thu #2

• Probability {Milk}: 5/8 ~ 60%• Probability {Milk given Bread & Butter }: 2/2

= 100%

Can we learn more at an aggregated daily/store level?

{Beer Diapers} • Probability {Diapers}= 6/8 =75% • Probability {Diapers given Beer)= 3/4=75%,

Lift = 0%

• Lift=40%: (When selling Bread & Butter the probability for selling milk increases by 40%, but it applies to only 2 transactions)

P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S

Purchase Example (sourced from Wikipedia)

• No correlation is found at a transactional level

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P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S

Purchase Example (cont.)

• Reflects a non-transactional consumption preference pattern

{Beer Diapers} are correlated at a store level

• Is this pattern significant (statistically)?

• How can we use it at the chain level to analyze the stores’ performance?

• Let’s check this pattern across all stores

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BreadButter

DiapersBeer

Mon #1

Tue #1

Wed #1

Thu #1

Milk

Mon #1

Mon #2

Tue #1

Tue #2

Wed #1

Wed #2

Thu #1

Thu #2

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Consumer Purchasing Patterns at a Store level(Beer & Diapers example - out of trillions of combinations)

• Automatically analyzingmillions of consumption preference patterns

• Root cause of anomalies:

- Operational failures

- Availability issues- Other local effects


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Basket Analysis (Individual behavior)• Focused on individual purchase behavior of a customer • Personal Applications: Cross-Selling & Up-Selling, personalized coupons,

personalized emails• General Applications: Store Design, Loyalty programs, Promotional plans…


Bottom Line: Benefits of both Methods

In-Store Purchase Pattern Analysis (store behavior) • Does not require transactional & personalized data (faster & simpler POS data)• Reflects non-transactional patterns (even by different customers)• Lower Error Rate (aggregation reduces false rules by random associations of

products)• More effective for store behavior analysis – correcting operational failures,

availability issues, localizing assortment, analyzing local effects

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DEMO

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Proven to increase same store growth by 0.8 to 3%

Uncover local consumer purchasing patterns within simple sales and inventory data in order to:

• Fine tune assortment at a store level to better fulfill local preferences

• Detect + correct in store operational anomalies that prevent high volume sales

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No Hardware

Automated

No External Data

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• Chain of 52 convenience stores in Muskegon, Michigan• Family owned• Started by Bud Westgate in 1952 with a single store and 3 used gas pumps• Continuous growth, expanded to include:

• Distribution center• Central bakery and deli• 6 Subway locations• Bulk fuel and propane business + Wesco Energy division

• For 55 years the mission has been Q-PPAS: Quality People, Products, Associates and Service

P R E D I C T I O N S & I N S I G H T S T H R O U G H P A T T E R N S1 4 |

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Pilot: Your total effort is less than a day

2 hours

Extract Data

Wesco

Generate Initial Recommendati

ons

3 hours

CB4Filter &

DistributeRecommendati

ons

1/2 Day

TogetherROI and

A/B Testing Report

5 min

CB4Recommendations

Sent to select stores.

Feedback collected

On-going

Stores

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1-2 DAYS• Installation• Configuration of data

extract• Integration of business

constraints into the analysis to increase the relevance of recommendations for store and merchandising managers

Deployment in less than a week


1. Setup & Installation

2. Training 3. App Deployment

Uncovers behavioral patternsand translates them into recommendations*Available on premise as well

5 DAYS• Training

- Solution owner/users – 5h- District manager – 3h- Store managers – 0.5h- Merchandising team – 2h

• Perform store rides• Perform several dry runs• Schedule automatic deployment

of recommendations to stores via email, back office PC, or mobile app

1 HOUR• Installation of back office or

mobile app• App is straightforward and

does not require dedicated training (comes with manual and video clips that walk the store managers through the process)

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Web ConsoleAbility to schedule and deploy

recommendations + review dashboards that measure ROI and

revenue lift

Point of Sale Data

CB4 ServersUncovers behavioral patterns

and translates them into actionable recommendations

Store managers/supervisorsRecommendations to resolve

operational opportunities

Merchandising ManagersRecommendations that help to

localize assortments and planograms

Feedback from stakeholders is tracked and measured


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App - Demo


Review your list of “Open Tasks”

Tap on each task to see details

Submit findings

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$1,910,432Estimated annual revenue

Lift2%

Operational opportunities detected 582

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HIT RATE

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DYNAMICALLY LOCALIZED

ASSORTMENT• 200 new items

introduced to assortment• Improved planogram

execution

IMPROVED CUSTOMER EXPERIENCE

• Store associate awareness to product display & operational opportunities.

• Weekly feedbacks

0.8 – 3% SAME STORE

GROWTH• On track to $2M

same store growth by end of fiscal year (2.2% sales increase)

IMPROVED AVAILABILITY• Increase

availability in stores by 7%

• Better Supply chain decisions

Value & Benefits


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Visit us at booth #4252 for more information

An17 exhibitor insights_tuesday_whats hiding in your point of sale data

Retail

Transcript of An17 exhibitor insights_tuesday_whats hiding in your point of sale data